Step 1 preparing input

Step 2: Loading Data and running model

Preparing sampling

At this point, let's just take random samples, in the future I can change this so I can decide what proportion of each label I want to pick; that is, setting a weight for each label.

It is important to note that in order to make random kernel truly random, we need to explicitly pass the random seed to them. Otherwise the random states for all kernels (i.e. get_sample below) will have the same random states, leading to the same results on different runs.

Model v02

Further

  1. 600 Characters abstract on my stuff
  2. Report: All of Jonathan's work. One paragraph, no more than 6 lines, about the machine learning/image processing

Write the outline of one of these papers

  1. What is the paper about
  2. what are the experiments in the papers.

The focus in additive, extracting features and seeing which are more predictive

Model v03